Applications of Commonly Used Numerical Techniques in Diatom-Based Paleoecology
Bibliographic record
Abstract
Applications of commonly used numerical techniques in diatom-based paleoecology are reviewed including: approaches used to model diatom taxa to important limnological variables; ordination and other commonly used multivariate approaches; and the myriad of approaches that are now being explored to infer environmental variables based on diatom assemblages. Modelling the response of individual diatom taxa to limnologically important variables is consistent with ecological theory and has been largely accomplished using approaches based on generalized linear models. These techniques have established that strong and significant relationships exist between the numerically dominant diatom taxa and important limnological variables (e.g., pH, nutrients, salinity). Null modelling approaches have also been used. However, inclusion of rare taxa in null models results in high rates of type-II errors, and consequently spurious claims that only a minority of diatoms have significant relationships to important limnological variables such as lakewater pH and nutrients. A variety of ordination techniques are widely used in diatom-based paleolimnological studies to aid in summarizing the main directions of variation in diatom assemblages, and to identify limnological variables that are strongly correlated to the diatom assemblages, both in time and space. More advanced ordination techniques, such as partial ordinations, are increasingly being used to assess the shared and unique variance attributable to groups of important limnological variables. Further, diatom-based approaches based on experimental designs with control lakes and appropriate multivariate statistics are now becoming increasingly common to assess, for example, the impact of forestry on water quality. A number of different diatom-based inference models based on the present-day relationships between diatom assemblages and limnological variables are now available for inferring important limnological variables. These approaches vary from simple approaches such as weighted-averaging to more complex approaches involving curve fitting and maximum likelihood, neural networks, and Bayesian statistics. All of these approaches have been shown to result in strong inference models, each using aspects of ecological information available from the diatom assemblages.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".